store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_757192', 'seed': 757192, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[7181, 50101, 20974, 1910, 14515, 15107, 47329, 29528, 37080, 20219, 45443, 45011, 44941, 52768, 16672, 14475, 49851, 52848, 18666, 20820]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6679, 'nll': 2.03680697593689}, 'chosen_samples': [34605, 21176, 31272, 30859, 12153, 528, 16663, 36149, 35443, 35970, 42050, 33221, 48852, 2010, 3868, 42539, 28944, 35924, 39074, 4212, 7481, 28453, 26689, 2675, 30431, 34508, 4914, 22491, 36120, 57257, 42213, 27622, 6123, 17071, 55404, 24570, 6948, 57898, 28760, 53048], 'chosen_samples_score': ['1.0754218', '1.0759127', '1.0799763', '1.0784035', '1.0807679', '1.0773755', '1.0794442', '1.0806823', '1.0783117', '1.0811865', '1.0781459', '1.0864878', '1.0837734', '1.08294', '1.0865425', '1.1164379', '1.1907542', '1.1099954', '1.1119826', '1.1648571', '1.1111841', '1.0877513', '1.1976951', '1.193204', '1.1042159', '1.0901792', '1.1311438', '1.2258956', '1.1849663', '1.0877738', '1.0974014', '1.1021476', '1.1512737', '1.1309898', '1.1563559', '1.1621569', '1.094568', '1.1528869', '1.0882423', '1.1543058']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7175, 'nll': 1.3495151390075684}, 'chosen_samples': [34058, 59460, 14213, 11911, 3791, 34829, 7113, 29390, 8680, 51646, 33923, 51730, 19619, 41860, 14866, 1430, 20273, 29002, 17404, 47494, 28469, 48241, 19947, 10210, 19501, 52953, 34078, 12379, 47741, 49809, 33593, 59449, 49955, 32481, 3719, 54350, 6428, 14650, 27169, 14635], 'chosen_samples_score': ['0.8100374', '0.81019264', '0.8104291', '0.8122016', '0.812035', '0.820999', '0.81284523', '0.8203066', '0.81356937', '0.8114754', '0.8144353', '0.8183374', '0.81326187', '0.81683224', '0.822457', '0.84713745', '0.8652272', '0.874931', '0.83383423', '0.8795884', '0.835132', '0.91195047', '0.9391556', '0.8899451', '0.8355116', '0.83796716', '0.8404382', '0.8321707', '0.8273872', '0.8804315', '0.8828508', '0.8379045', '0.8921306', '0.82385266', '0.8299683', '0.82623553', '0.9538612', '0.82594985', '0.8595117', '0.8520164']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7049, 'nll': 1.256308966064453}, 'chosen_samples': [23883, 49438, 32289, 20054, 30332, 31094, 47517, 30322, 25234, 6852, 25823, 4076, 57837, 31875, 22661, 12856, 340, 27795, 59468, 13306, 3336, 19244, 6152, 6994, 10156, 6776, 59257, 39333, 26546, 27002, 4256, 57754, 670, 25144, 6988, 34608, 5013, 2817, 31863, 27053], 'chosen_samples_score': ['0.71275', '0.7131703', '0.7132232', '0.71565294', '0.72120667', '0.7137463', '0.72049356', '0.7260999', '0.716545', '0.7205013', '0.72234577', '0.71634644', '0.72896516', '0.74374473', '0.7386178', '0.73265016', '0.7311368', '0.74350214', '0.7428129', '0.7338877', '0.733475', '0.737151', '0.744864', '0.7572426', '0.7554075', '0.7510053', '0.79652', '0.7771944', '0.7987134', '0.7587174', '0.7581617', '0.7874396', '0.7620251', '0.82208455', '0.7565666', '0.7994217', '0.77524394', '0.761217', '0.80409557', '0.8418289']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8363, 'nll': 0.7966622532844544}, 'chosen_samples': [43351, 52011, 45047, 14269, 3524, 8812, 34822, 27716, 2409, 3370, 49624, 48158, 59706, 45800, 40169, 17153, 32693, 54532, 48598, 21353, 33162, 51719, 20847, 34461, 11708, 53026, 2034, 28373, 45456, 12345, 36562, 32022, 2000, 29061, 8702, 19792, 51038, 53491, 42703, 52169], 'chosen_samples_score': ['0.8224561', '0.8230708', '0.82488704', '0.8231666', '0.8237827', '0.82580787', '0.8301145', '0.8263029', '0.8305784', '0.8672452', '0.84071624', '0.86867696', '0.8397491', '0.8404894', '0.8488176', '0.85952514', '0.83813167', '0.8592004', '0.83401096', '0.84940094', '0.83878845', '0.84657234', '0.83335906', '0.8400131', '0.8399657', '0.8705447', '0.90428996', '0.87370986', '0.8976357', '0.9000911', '0.9241889', '0.87365764', '0.94870025', '0.8903101', '0.9013404', '0.8712397', '0.9393351', '0.93363595', '0.89538056', '0.9327459']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.868, 'nll': 0.6867672558784484}, 'chosen_samples': [59651, 37720, 9180, 21307, 8464, 30439, 21174, 54885, 9451, 8178, 34070, 35098, 8093, 13073, 42892, 33995, 30962, 26444, 2184, 37048, 8045, 45851, 53106, 59343, 55483, 29843, 24533, 31585, 12514, 718, 7851, 19360, 3730, 17043, 37696, 17528, 32519, 44331, 37277, 28470], 'chosen_samples_score': ['0.7857469', '0.7859948', '0.79052', '0.79541194', '0.7904496', '0.7903293', '0.78997856', '0.7912329', '0.7970209', '0.80195487', '0.80278116', '0.80619955', '0.79721755', '0.8018541', '0.79809487', '0.8067666', '0.8073901', '0.816678', '0.8128505', '0.8208338', '0.8087991', '0.8176887', '0.81186795', '0.8295555', '0.8170903', '0.8292755', '0.83451694', '0.82892495', '0.8328952', '0.8233231', '0.83044606', '0.81753176', '0.8189352', '0.8123034', '0.83727545', '0.854475', '0.86252725', '0.8945876', '0.9080666', '0.9203279']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9196, 'nll': 0.5099132907867432}, 'chosen_samples': [54932, 56006, 31103, 57474, 15114, 17178, 39778, 7710, 45048, 59814, 42337, 15858, 22591, 16756, 12297, 51544, 57728, 2548, 42774, 57718, 34847, 45118, 34520, 51764, 43126, 37373, 15402, 134, 59574, 54576, 39674, 32776, 1459, 22083, 25748, 46368, 4958, 14722, 18324, 32002], 'chosen_samples_score': ['0.87252384', '0.8766777', '0.87823164', '0.87856257', '0.87887', '0.88499445', '0.8791073', '0.8878088', '0.88514894', '0.8787513', '0.8796207', '0.8801853', '0.8894108', '1.0475543', '0.90142834', '0.9231459', '0.9122933', '0.88978004', '0.9583916', '0.9168861', '0.9401957', '0.98464817', '1.0954403', '0.93247604', '0.94868916', '0.91979176', '0.9057476', '0.91953397', '0.9293748', '0.8934525', '0.90869457', '1.0026038', '0.930925', '0.99321914', '0.9007834', '0.931546', '0.98317915', '0.91797876', '0.8990827', '0.9029313']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9388, 'nll': 0.41799740200042723}, 'chosen_samples': [21896, 31301, 36417, 38389, 1423, 50370, 42112, 59294, 2381, 4761, 4955, 46313, 2302, 16909, 40012, 50576, 59681, 13176, 42787, 17182, 30177, 19945, 57480, 49545, 44286, 57575, 57053, 57742, 40654, 20569, 274, 52089, 36415, 16628, 37050, 5536, 7168, 2765, 10982, 15723], 'chosen_samples_score': ['0.7524068', '0.75285435', '0.75364786', '0.7538554', '0.7580992', '0.75983936', '0.7548026', '0.7561159', '0.76131874', '0.76256573', '0.7641995', '0.7628477', '0.763214', '0.7633446', '0.7646133', '0.7650078', '0.7734293', '0.803304', '0.88994616', '0.7883656', '0.76898754', '0.81024873', '0.8436523', '0.7862546', '0.77029294', '0.77839017', '0.8851325', '0.84334636', '0.76503384', '0.77053654', '0.860876', '0.85174924', '0.7989655', '0.8253687', '0.77799827', '0.82219994', '0.8150593', '0.80822814', '0.7743675', '0.7849604']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9592, 'nll': 0.3287425730705261}, 'chosen_samples': [43043, 11572, 38408, 53844, 40481, 33780, 13714, 47870, 26017, 50930, 57732, 55620, 30764, 3056, 19868, 34500, 44262, 43998, 53116, 21066, 27307, 57523, 54858, 59401, 22053, 36984, 55739, 19888, 47643, 35643, 1518, 56014, 32335, 52690, 56468, 13655, 33812, 28357, 3644, 28719], 'chosen_samples_score': ['0.8476574', '0.84825295', '0.85453403', '0.85447365', '0.8941263', '0.8641856', '0.8697005', '0.8940133', '0.8586295', '0.8515776', '0.8735547', '0.88841975', '0.88518107', '0.8684054', '0.8669523', '0.852248', '0.86563635', '0.8602935', '0.89579344', '0.91482085', '0.8896137', '0.9019963', '0.899157', '0.9149367', '0.92020243', '0.9424673', '0.94686174', '1.0651433', '0.99170494', '0.9322032', '0.9161052', '0.94238', '0.9251584', '1.0114229', '0.93426394', '0.97063303', '0.9410468', '0.9277829', '0.9224582', '0.9456234']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9579, 'nll': 0.34578054237365724}, 'chosen_samples': [31794, 33505, 19344, 29711, 43194, 41933, 24940, 17010, 54802, 24360, 2118, 29952, 14367, 31664, 4153, 20171, 47475, 7924, 32880, 12995, 3676, 15630, 28930, 24943, 11711, 4165, 40589, 9633, 30545, 10256, 1573, 20881, 59980, 3756, 14765, 9552, 12184, 45099, 32573, 9472], 'chosen_samples_score': ['0.76792485', '0.7824965', '0.7858663', '0.7689293', '0.7793734', '0.7719713', '0.77121', '0.7864727', '0.77888113', '0.7819501', '0.77924323', '0.77211934', '0.7808686', '0.7915196', '0.79153454', '0.849793', '0.80302304', '0.79524785', '0.8364677', '0.8648455', '0.8422047', '0.8379551', '0.80745465', '0.8361314', '0.82899517', '0.8881095', '0.94862175', '0.79895145', '0.8123368', '0.79284906', '0.90643173', '0.8380156', '0.8668819', '0.81270015', '0.8685032', '0.81861436', '0.8671017', '0.8299457', '0.79796946', '0.88544023']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9625, 'nll': 0.3356996600151062}, 'chosen_samples': [5175, 49744, 52086, 55438, 13153, 17540, 605, 51298, 31102, 29530, 43745, 50231, 31738, 20903, 17739, 21426, 30884, 52661, 47949, 22562, 41307, 52358, 36408, 5683, 51337, 44135, 24462, 13149, 966, 15450, 28368, 39668, 9147, 23956, 44099, 34328, 51180, 37065, 37469, 59701], 'chosen_samples_score': ['0.9097011', '0.9111698', '0.9100061', '0.91689336', '0.9180866', '0.9174647', '0.9247257', '0.9304265', '0.9298894', '0.93117154', '0.9290738', '0.9316088', '0.96901226', '0.93426013', '0.9449143', '0.9331551', '1.0241892', '0.9802837', '0.93811756', '0.94278747', '1.0008113', '0.9369444', '0.9349952', '0.9514392', '1.0867423', '1.0117209', '0.9543056', '0.94715434', '0.9747167', '0.95319706', '1.0215473', '0.9322825', '1.0093541', '1.0555977', '1.0410348', '0.95848674', '1.0120775', '0.945602', '1.0808024', '0.97187334']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9616, 'nll': 0.3207848826408386}, 'chosen_samples': [30758, 20169, 20720, 57242, 808, 42963, 34676, 50714, 21642, 49002, 10301, 32323, 30844, 12059, 22531, 20924, 48767, 32047, 44442, 49153, 22607, 41453, 38355, 56401, 9431, 6582, 41789, 50826, 12066, 42199, 4822, 5298, 46613, 34406, 59747, 49563, 35917, 19934, 1276, 45056], 'chosen_samples_score': ['0.8255342', '0.8407808', '0.8318821', '0.832061', '0.8403033', '0.82585055', '0.84049094', '0.8387049', '0.82578605', '0.8283622', '0.8362683', '0.8410548', '0.84132093', '0.8673696', '0.9237752', '0.85786974', '0.855372', '0.98674536', '0.8574488', '0.86020416', '0.88758737', '0.857567', '0.88980377', '0.8514822', '0.85492694', '0.8478921', '0.9486945', '0.84232706', '0.8663858', '0.8508687', '0.9946092', '0.9707028', '0.8701445', '0.8700217', '0.9140867', '0.8597556', '0.8736122', '0.8533015', '0.88508976', '0.84188414']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9699, 'nll': 0.2689818591594696}, 'chosen_samples': [49215, 31742, 56962, 8920, 20792, 45954, 45944, 2466, 43072, 36450, 54950, 44870, 46021, 49908, 11795, 43198, 49573, 50916, 17494, 35246, 26302, 21436, 35401, 1160, 53736, 6231, 34716, 51144, 28374, 39411, 34916, 49672, 11534, 18473, 42415, 44534, 31252, 49910, 45586, 40536], 'chosen_samples_score': ['0.87573355', '0.8760827', '0.87744534', '0.8807635', '0.88280296', '0.8813151', '0.88240904', '0.88217735', '0.8842468', '0.90998936', '0.88525575', '0.9040158', '0.93631136', '0.885668', '0.8898554', '0.93699336', '0.9299775', '0.90125483', '0.92990685', '0.9147553', '0.89193135', '0.9141349', '0.93650717', '0.92857736', '0.88730484', '0.9038683', '0.93614155', '0.9376714', '0.98038083', '1.0483463', '0.97501063', '0.982676', '1.0241958', '1.0191538', '1.0740087', '0.98890465', '1.0633173', '0.9850645', '0.95751417', '0.9820968']})
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